Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations7719
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.6 MiB
Average record size in memory1.1 KiB

Variable types

Numeric6
Text8
DateTime2
Categorical8

Alerts

Category is highly overall correlated with Sub-CategoryHigh correlation
Market is highly overall correlated with RegionHigh correlation
Region is highly overall correlated with MarketHigh correlation
Row ID is highly overall correlated with Shipping CostHigh correlation
Sales is highly overall correlated with Shipping CostHigh correlation
Shipping Cost is highly overall correlated with Row ID and 1 other fieldsHigh correlation
Sub-Category is highly overall correlated with CategoryHigh correlation
Row ID has unique values Unique
Discount has 4504 (58.3%) zeros Zeros
Profit has 101 (1.3%) zeros Zeros

Reproduction

Analysis started2025-01-23 12:04:54.756750
Analysis finished2025-01-23 12:05:01.331035
Duration6.57 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Row ID
Real number (ℝ)

High correlation  Unique 

Distinct7719
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5588.1985
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.4 KiB
2025-01-23T17:35:01.463116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1384.8
Q13424.5
median5651
Q37826.5
95-th percentile9565.1
Maximum9999
Range9998
Interquartile range (IQR)4402

Descriptive statistics

Standard deviation2610.2139
Coefficient of variation (CV)0.46709399
Kurtosis-1.0934312
Mean5588.1985
Median Absolute Deviation (MAD)2202
Skewness-0.10128571
Sum43135304
Variance6813216.6
MonotonicityStrictly increasing
2025-01-23T17:35:01.668390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
7155 1
 
< 0.1%
7116 1
 
< 0.1%
7114 1
 
< 0.1%
7111 1
 
< 0.1%
7110 1
 
< 0.1%
7108 1
 
< 0.1%
7107 1
 
< 0.1%
7106 1
 
< 0.1%
7105 1
 
< 0.1%
Other values (7709) 7709
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
17 1
< 0.1%
22 1
< 0.1%
29 1
< 0.1%
33 1
< 0.1%
35 1
< 0.1%
37 1
< 0.1%
ValueCountFrequency (%)
9999 1
< 0.1%
9998 1
< 0.1%
9997 1
< 0.1%
9996 1
< 0.1%
9995 1
< 0.1%
9994 1
< 0.1%
9992 1
< 0.1%
9991 1
< 0.1%
9990 1
< 0.1%
9989 1
< 0.1%
Distinct5985
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Memory size604.2 KiB
2025-01-23T17:35:01.946766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length23
Mean length23.137842
Min length20

Characters and Unicode

Total characters178601
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4777 ?
Unique (%)61.9%

Sample

1st rowCA-2014-AB10015140-41954
2nd rowCA-2014-AB10015140-41954
3rd rowCA-2012-AB10015140-40974
4th rowCA-2012-AB10015140-40974
5th rowCA-2012-AB10015140-40958
ValueCountFrequency (%)
ca-2015-ac10615140-42250 11
 
0.1%
ca-2014-ag10270140-41896 9
 
0.1%
ca-2013-bt11680140-41464 9
 
0.1%
ca-2012-aj10795140-41271 7
 
0.1%
ca-2014-ai10855140-41954 7
 
0.1%
ca-2015-am10705140-42207 7
 
0.1%
ca-2013-bs11665140-41595 7
 
0.1%
ca-2015-as10225140-42265 7
 
0.1%
ca-2012-bh11710140-41068 7
 
0.1%
ca-2015-bs11800140-42104 6
 
0.1%
Other values (5975) 7642
99.0%
2025-01-23T17:35:02.381651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 28019
15.7%
- 23157
13.0%
0 19382
10.9%
2 19269
10.8%
4 16725
9.4%
5 12063
 
6.8%
3 7389
 
4.1%
8 6066
 
3.4%
9 5634
 
3.2%
7 5282
 
3.0%
Other values (30) 35615
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 178601
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 28019
15.7%
- 23157
13.0%
0 19382
10.9%
2 19269
10.8%
4 16725
9.4%
5 12063
 
6.8%
3 7389
 
4.1%
8 6066
 
3.4%
9 5634
 
3.2%
7 5282
 
3.0%
Other values (30) 35615
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 178601
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 28019
15.7%
- 23157
13.0%
0 19382
10.9%
2 19269
10.8%
4 16725
9.4%
5 12063
 
6.8%
3 7389
 
4.1%
8 6066
 
3.4%
9 5634
 
3.2%
7 5282
 
3.0%
Other values (30) 35615
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 178601
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 28019
15.7%
- 23157
13.0%
0 19382
10.9%
2 19269
10.8%
4 16725
9.4%
5 12063
 
6.8%
3 7389
 
4.1%
8 6066
 
3.4%
9 5634
 
3.2%
7 5282
 
3.0%
Other values (30) 35615
19.9%
Distinct1290
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size60.4 KiB
Minimum2012-01-01 00:00:00
Maximum2015-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-23T17:35:02.563192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:35:02.769775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1384
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size60.4 KiB
Minimum2012-01-03 00:00:00
Maximum2016-01-06 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-23T17:35:02.947969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:35:03.166701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Ship Mode
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size524.6 KiB
Standard Class
4004 
Second Class
1690 
First Class
1505 
Same Day
520 

Length

Max length14
Median length14
Mean length12.573002
Min length8

Characters and Unicode

Total characters97051
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFirst Class
2nd rowFirst Class
3rd rowFirst Class
4th rowFirst Class
5th rowStandard Class

Common Values

ValueCountFrequency (%)
Standard Class 4004
51.9%
Second Class 1690
21.9%
First Class 1505
 
19.5%
Same Day 520
 
6.7%

Length

2025-01-23T17:35:03.344267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-23T17:35:03.513748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
class 7199
46.6%
standard 4004
25.9%
second 1690
 
10.9%
first 1505
 
9.7%
same 520
 
3.4%
day 520
 
3.4%

Most occurring characters

ValueCountFrequency (%)
a 16247
16.7%
s 15903
16.4%
d 9698
10.0%
7719
8.0%
l 7199
7.4%
C 7199
7.4%
S 6214
 
6.4%
n 5694
 
5.9%
r 5509
 
5.7%
t 5509
 
5.7%
Other values (8) 10160
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 97051
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 16247
16.7%
s 15903
16.4%
d 9698
10.0%
7719
8.0%
l 7199
7.4%
C 7199
7.4%
S 6214
 
6.4%
n 5694
 
5.9%
r 5509
 
5.7%
t 5509
 
5.7%
Other values (8) 10160
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 97051
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 16247
16.7%
s 15903
16.4%
d 9698
10.0%
7719
8.0%
l 7199
7.4%
C 7199
7.4%
S 6214
 
6.4%
n 5694
 
5.9%
r 5509
 
5.7%
t 5509
 
5.7%
Other values (8) 10160
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 97051
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 16247
16.7%
s 15903
16.4%
d 9698
10.0%
7719
8.0%
l 7199
7.4%
C 7199
7.4%
S 6214
 
6.4%
n 5694
 
5.9%
r 5509
 
5.7%
t 5509
 
5.7%
Other values (8) 10160
10.5%
Distinct5009
Distinct (%)64.9%
Missing0
Missing (%)0.0%
Memory size507.7 KiB
2025-01-23T17:35:03.943321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.336183
Min length7

Characters and Unicode

Total characters79785
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3520 ?
Unique (%)45.6%

Sample

1st rowAB-100151402
2nd rowAB-100151402
3rd rowAB-100151404
4th rowAB-100151404
5th rowAB-100151402
ValueCountFrequency (%)
ap-109151404 22
 
0.3%
bm-116501402 16
 
0.2%
ai-108551404 15
 
0.2%
ac-106151406 13
 
0.2%
bt-116801406 13
 
0.2%
bd-113201404 13
 
0.2%
aj-107951404 12
 
0.2%
bc-111251404 11
 
0.1%
bp-110951404 11
 
0.1%
as-100901406 10
 
0.1%
Other values (4999) 7583
98.2%
2025-01-23T17:35:04.373651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12937
16.2%
0 9166
11.5%
- 7719
9.7%
5 7241
 
9.1%
4 5414
 
6.8%
2 4884
 
6.1%
8 4089
 
5.1%
7 3331
 
4.2%
3 3250
 
4.1%
9 3165
 
4.0%
Other values (30) 18589
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 79785
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 12937
16.2%
0 9166
11.5%
- 7719
9.7%
5 7241
 
9.1%
4 5414
 
6.8%
2 4884
 
6.1%
8 4089
 
5.1%
7 3331
 
4.2%
3 3250
 
4.1%
9 3165
 
4.0%
Other values (30) 18589
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 79785
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 12937
16.2%
0 9166
11.5%
- 7719
9.7%
5 7241
 
9.1%
4 5414
 
6.8%
2 4884
 
6.1%
8 4089
 
5.1%
7 3331
 
4.2%
3 3250
 
4.1%
9 3165
 
4.0%
Other values (30) 18589
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 79785
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 12937
16.2%
0 9166
11.5%
- 7719
9.7%
5 7241
 
9.1%
4 5414
 
6.8%
2 4884
 
6.1%
8 4089
 
5.1%
7 3331
 
4.2%
3 3250
 
4.1%
9 3165
 
4.0%
Other values (30) 18589
23.3%
Distinct792
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size531.0 KiB
2025-01-23T17:35:04.744395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length18
Mean length12.953621
Min length7

Characters and Unicode

Total characters99989
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st rowAaron Bergman
2nd rowAaron Bergman
3rd rowAaron Bergman
4th rowAaron Bergman
5th rowAaron Bergman
ValueCountFrequency (%)
brian 126
 
0.8%
alan 116
 
0.7%
barry 116
 
0.7%
anthony 102
 
0.7%
bill 98
 
0.6%
anna 89
 
0.6%
adam 82
 
0.5%
arthur 79
 
0.5%
ben 77
 
0.5%
paul 76
 
0.5%
Other values (899) 14519
93.8%
2025-01-23T17:35:05.245844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 9119
 
9.1%
e 8990
 
9.0%
n 8209
 
8.2%
7761
 
7.8%
r 7468
 
7.5%
i 5671
 
5.7%
l 5296
 
5.3%
o 4687
 
4.7%
t 4119
 
4.1%
s 3496
 
3.5%
Other values (47) 35173
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 99989
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 9119
 
9.1%
e 8990
 
9.0%
n 8209
 
8.2%
7761
 
7.8%
r 7468
 
7.5%
i 5671
 
5.7%
l 5296
 
5.3%
o 4687
 
4.7%
t 4119
 
4.1%
s 3496
 
3.5%
Other values (47) 35173
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 99989
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 9119
 
9.1%
e 8990
 
9.0%
n 8209
 
8.2%
7761
 
7.8%
r 7468
 
7.5%
i 5671
 
5.7%
l 5296
 
5.3%
o 4687
 
4.7%
t 4119
 
4.1%
s 3496
 
3.5%
Other values (47) 35173
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 99989
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 9119
 
9.1%
e 8990
 
9.0%
n 8209
 
8.2%
7761
 
7.8%
r 7468
 
7.5%
i 5671
 
5.7%
l 5296
 
5.3%
o 4687
 
4.7%
t 4119
 
4.1%
s 3496
 
3.5%
Other values (47) 35173
35.2%

Segment
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size496.4 KiB
Consumer
4005 
Corporate
2358 
Home Office
1356 

Length

Max length11
Median length8
Mean length8.8324913
Min length8

Characters and Unicode

Total characters68178
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConsumer
2nd rowConsumer
3rd rowConsumer
4th rowConsumer
5th rowConsumer

Common Values

ValueCountFrequency (%)
Consumer 4005
51.9%
Corporate 2358
30.5%
Home Office 1356
 
17.6%

Length

2025-01-23T17:35:05.408920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-23T17:35:05.542050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
consumer 4005
44.1%
corporate 2358
26.0%
home 1356
 
14.9%
office 1356
 
14.9%

Most occurring characters

ValueCountFrequency (%)
o 10077
14.8%
e 9075
13.3%
r 8721
12.8%
C 6363
9.3%
m 5361
7.9%
n 4005
 
5.9%
s 4005
 
5.9%
u 4005
 
5.9%
f 2712
 
4.0%
t 2358
 
3.5%
Other values (7) 11496
16.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 68178
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 10077
14.8%
e 9075
13.3%
r 8721
12.8%
C 6363
9.3%
m 5361
7.9%
n 4005
 
5.9%
s 4005
 
5.9%
u 4005
 
5.9%
f 2712
 
4.0%
t 2358
 
3.5%
Other values (7) 11496
16.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 68178
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 10077
14.8%
e 9075
13.3%
r 8721
12.8%
C 6363
9.3%
m 5361
7.9%
n 4005
 
5.9%
s 4005
 
5.9%
u 4005
 
5.9%
f 2712
 
4.0%
t 2358
 
3.5%
Other values (7) 11496
16.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 68178
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 10077
14.8%
e 9075
13.3%
r 8721
12.8%
C 6363
9.3%
m 5361
7.9%
n 4005
 
5.9%
s 4005
 
5.9%
u 4005
 
5.9%
f 2712
 
4.0%
t 2358
 
3.5%
Other values (7) 11496
16.9%

City
Text

Distinct1992
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Memory size506.1 KiB
2025-01-23T17:35:05.812080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length22
Mean length8.4271279
Min length3

Characters and Unicode

Total characters65049
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique785 ?
Unique (%)10.2%

Sample

1st rowOklahoma City
2nd rowOklahoma City
3rd rowSeattle
4th rowSeattle
5th rowArlington
ValueCountFrequency (%)
city 300
 
3.1%
san 261
 
2.7%
los 183
 
1.9%
york 174
 
1.8%
new 173
 
1.8%
angeles 170
 
1.8%
francisco 96
 
1.0%
philadelphia 78
 
0.8%
de 76
 
0.8%
manila 73
 
0.8%
Other values (2111) 8048
83.6%
2025-01-23T17:35:06.297177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 7911
 
12.2%
n 5116
 
7.9%
e 5085
 
7.8%
o 4574
 
7.0%
i 4019
 
6.2%
r 3599
 
5.5%
l 3218
 
4.9%
s 2390
 
3.7%
t 2387
 
3.7%
u 2283
 
3.5%
Other values (61) 24467
37.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65049
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 7911
 
12.2%
n 5116
 
7.9%
e 5085
 
7.8%
o 4574
 
7.0%
i 4019
 
6.2%
r 3599
 
5.5%
l 3218
 
4.9%
s 2390
 
3.7%
t 2387
 
3.7%
u 2283
 
3.5%
Other values (61) 24467
37.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65049
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 7911
 
12.2%
n 5116
 
7.9%
e 5085
 
7.8%
o 4574
 
7.0%
i 4019
 
6.2%
r 3599
 
5.5%
l 3218
 
4.9%
s 2390
 
3.7%
t 2387
 
3.7%
u 2283
 
3.5%
Other values (61) 24467
37.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65049
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 7911
 
12.2%
n 5116
 
7.9%
e 5085
 
7.8%
o 4574
 
7.0%
i 4019
 
6.2%
r 3599
 
5.5%
l 3218
 
4.9%
s 2390
 
3.7%
t 2387
 
3.7%
u 2283
 
3.5%
Other values (61) 24467
37.6%

State
Text

Distinct709
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Memory size534.3 KiB
2025-01-23T17:35:06.609500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length33
Mean length10.38321
Min length3

Characters and Unicode

Total characters80148
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique171 ?
Unique (%)2.2%

Sample

1st rowOklahoma
2nd rowOklahoma
3rd rowWashington
4th rowWashington
5th rowTexas
ValueCountFrequency (%)
california 387
 
3.7%
new 380
 
3.6%
england 299
 
2.9%
south 208
 
2.0%
ile-de-france 205
 
2.0%
york 198
 
1.9%
north 174
 
1.7%
wales 155
 
1.5%
queensland 144
 
1.4%
rhine-westphalia 136
 
1.3%
Other values (793) 8142
78.1%
2025-01-23T17:35:07.175094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 11393
 
14.2%
n 6468
 
8.1%
e 5543
 
6.9%
i 5479
 
6.8%
r 4595
 
5.7%
o 4384
 
5.5%
l 3871
 
4.8%
t 3350
 
4.2%
s 3015
 
3.8%
2709
 
3.4%
Other values (66) 29341
36.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80148
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 11393
 
14.2%
n 6468
 
8.1%
e 5543
 
6.9%
i 5479
 
6.8%
r 4595
 
5.7%
o 4384
 
5.5%
l 3871
 
4.8%
t 3350
 
4.2%
s 3015
 
3.8%
2709
 
3.4%
Other values (66) 29341
36.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80148
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 11393
 
14.2%
n 6468
 
8.1%
e 5543
 
6.9%
i 5479
 
6.8%
r 4595
 
5.7%
o 4384
 
5.5%
l 3871
 
4.8%
t 3350
 
4.2%
s 3015
 
3.8%
2709
 
3.4%
Other values (66) 29341
36.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80148
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 11393
 
14.2%
n 6468
 
8.1%
e 5543
 
6.9%
i 5479
 
6.8%
r 4595
 
5.7%
o 4384
 
5.5%
l 3871
 
4.8%
t 3350
 
4.2%
s 3015
 
3.8%
2709
 
3.4%
Other values (66) 29341
36.6%
Distinct132
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size496.1 KiB
2025-01-23T17:35:07.470932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length22
Mean length8.7988081
Min length4

Characters and Unicode

Total characters67918
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)0.3%

Sample

1st rowUnited States
2nd rowUnited States
3rd rowUnited States
4th rowUnited States
5th rowUnited States
ValueCountFrequency (%)
united 1857
18.0%
states 1531
14.9%
france 546
 
5.3%
australia 530
 
5.1%
mexico 427
 
4.1%
china 424
 
4.1%
germany 412
 
4.0%
kingdom 326
 
3.2%
india 320
 
3.1%
brazil 234
 
2.3%
Other values (140) 3690
35.8%
2025-01-23T17:35:07.970492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 8487
 
12.5%
i 6264
 
9.2%
e 6175
 
9.1%
t 6164
 
9.1%
n 5834
 
8.6%
d 3343
 
4.9%
r 2808
 
4.1%
s 2785
 
4.1%
2578
 
3.8%
o 2101
 
3.1%
Other values (44) 21379
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 67918
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 8487
 
12.5%
i 6264
 
9.2%
e 6175
 
9.1%
t 6164
 
9.1%
n 5834
 
8.6%
d 3343
 
4.9%
r 2808
 
4.1%
s 2785
 
4.1%
2578
 
3.8%
o 2101
 
3.1%
Other values (44) 21379
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 67918
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 8487
 
12.5%
i 6264
 
9.2%
e 6175
 
9.1%
t 6164
 
9.1%
n 5834
 
8.6%
d 3343
 
4.9%
r 2808
 
4.1%
s 2785
 
4.1%
2578
 
3.8%
o 2101
 
3.1%
Other values (44) 21379
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 67918
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 8487
 
12.5%
i 6264
 
9.2%
e 6175
 
9.1%
t 6164
 
9.1%
n 5834
 
8.6%
d 3343
 
4.9%
r 2808
 
4.1%
s 2785
 
4.1%
2578
 
3.8%
o 2101
 
3.1%
Other values (44) 21379
31.5%

Region
Categorical

High correlation 

Distinct23
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size524.3 KiB
Western Europe
1079 
Central America
816 
Oceania
632 
Western US
542 
Eastern Asia
499 
Other values (18)
4151 

Length

Max length17
Median length14
Mean length12.536468
Min length6

Characters and Unicode

Total characters96769
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCentral US
2nd rowCentral US
3rd rowWestern US
4th rowWestern US
5th rowCentral US

Common Values

ValueCountFrequency (%)
Western Europe 1079
14.0%
Central America 816
10.6%
Oceania 632
 
8.2%
Western US 542
 
7.0%
Eastern Asia 499
 
6.5%
Southeastern Asia 485
 
6.3%
Southern Asia 459
 
5.9%
Eastern US 457
 
5.9%
South America 397
 
5.1%
Southern Europe 356
 
4.6%
Other values (13) 1997
25.9%

Length

2025-01-23T17:35:08.141319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
europe 2001
13.8%
western 1835
12.6%
asia 1584
10.9%
us 1531
10.5%
eastern 1250
8.6%
central 1226
8.4%
america 1213
8.4%
southern 1104
7.6%
oceania 632
 
4.4%
southeastern 485
 
3.3%
Other values (6) 1660
11.4%

Most occurring characters

ValueCountFrequency (%)
e 12653
13.1%
r 10679
11.0%
a 8116
 
8.4%
t 7287
 
7.5%
n 7170
 
7.4%
6802
 
7.0%
s 5154
 
5.3%
o 4492
 
4.6%
i 4136
 
4.3%
u 3987
 
4.1%
Other values (16) 26293
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 96769
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 12653
13.1%
r 10679
11.0%
a 8116
 
8.4%
t 7287
 
7.5%
n 7170
 
7.4%
6802
 
7.0%
s 5154
 
5.3%
o 4492
 
4.6%
i 4136
 
4.3%
u 3987
 
4.1%
Other values (16) 26293
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 96769
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 12653
13.1%
r 10679
11.0%
a 8116
 
8.4%
t 7287
 
7.5%
n 7170
 
7.4%
6802
 
7.0%
s 5154
 
5.3%
o 4492
 
4.6%
i 4136
 
4.3%
u 3987
 
4.1%
Other values (16) 26293
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 96769
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 12653
13.1%
r 10679
11.0%
a 8116
 
8.4%
t 7287
 
7.5%
n 7170
 
7.4%
6802
 
7.0%
s 5154
 
5.3%
o 4492
 
4.6%
i 4136
 
4.3%
u 3987
 
4.1%
Other values (16) 26293
27.2%

Market
Categorical

High correlation 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size483.5 KiB
Asia Pacific
2216 
Europe
2001 
USCA
1582 
LATAM
1447 
Africa
473 

Length

Max length12
Median length6
Mean length7.1251457
Min length4

Characters and Unicode

Total characters54999
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSCA
2nd rowUSCA
3rd rowUSCA
4th rowUSCA
5th rowUSCA

Common Values

ValueCountFrequency (%)
Asia Pacific 2216
28.7%
Europe 2001
25.9%
USCA 1582
20.5%
LATAM 1447
18.7%
Africa 473
 
6.1%

Length

2025-01-23T17:35:08.305573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-23T17:35:08.456456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
asia 2216
22.3%
pacific 2216
22.3%
europe 2001
20.1%
usca 1582
15.9%
latam 1447
14.6%
africa 473
 
4.8%

Most occurring characters

ValueCountFrequency (%)
A 7165
13.0%
i 7121
12.9%
c 4905
 
8.9%
a 4905
 
8.9%
f 2689
 
4.9%
r 2474
 
4.5%
P 2216
 
4.0%
2216
 
4.0%
s 2216
 
4.0%
E 2001
 
3.6%
Other values (10) 17091
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54999
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 7165
13.0%
i 7121
12.9%
c 4905
 
8.9%
a 4905
 
8.9%
f 2689
 
4.9%
r 2474
 
4.5%
P 2216
 
4.0%
2216
 
4.0%
s 2216
 
4.0%
E 2001
 
3.6%
Other values (10) 17091
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54999
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 7165
13.0%
i 7121
12.9%
c 4905
 
8.9%
a 4905
 
8.9%
f 2689
 
4.9%
r 2474
 
4.5%
P 2216
 
4.0%
2216
 
4.0%
s 2216
 
4.0%
E 2001
 
3.6%
Other values (10) 17091
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54999
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 7165
13.0%
i 7121
12.9%
c 4905
 
8.9%
a 4905
 
8.9%
f 2689
 
4.9%
r 2474
 
4.5%
P 2216
 
4.0%
2216
 
4.0%
s 2216
 
4.0%
E 2001
 
3.6%
Other values (10) 17091
31.1%
Distinct2245
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Memory size512.7 KiB
2025-01-23T17:35:08.811467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters84909
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique934 ?
Unique (%)12.1%

Sample

1st rowTEC-PH-5816
2nd rowFUR-BO-5957
3rd rowFUR-CH-4421
4th rowOFF-AR-5309
5th rowOFF-ST-3078
ValueCountFrequency (%)
off-fa-6129 44
 
0.6%
off-st-4057 41
 
0.5%
fur-ch-5441 28
 
0.4%
off-st-6047 25
 
0.3%
off-st-6033 25
 
0.3%
off-st-5693 24
 
0.3%
off-st-4268 23
 
0.3%
off-st-4267 23
 
0.3%
off-st-6248 23
 
0.3%
fur-ch-4654 23
 
0.3%
Other values (2235) 7440
96.4%
2025-01-23T17:35:09.255695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 15438
18.2%
F 7859
 
9.3%
C 5193
 
6.1%
5 5099
 
6.0%
4 4468
 
5.3%
O 4408
 
5.2%
3 3972
 
4.7%
T 3879
 
4.6%
6 3313
 
3.9%
U 2860
 
3.4%
Other values (17) 28420
33.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84909
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 15438
18.2%
F 7859
 
9.3%
C 5193
 
6.1%
5 5099
 
6.0%
4 4468
 
5.3%
O 4408
 
5.2%
3 3972
 
4.7%
T 3879
 
4.6%
6 3313
 
3.9%
U 2860
 
3.4%
Other values (17) 28420
33.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84909
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 15438
18.2%
F 7859
 
9.3%
C 5193
 
6.1%
5 5099
 
6.0%
4 4468
 
5.3%
O 4408
 
5.2%
3 3972
 
4.7%
T 3879
 
4.6%
6 3313
 
3.9%
U 2860
 
3.4%
Other values (17) 28420
33.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84909
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 15438
18.2%
F 7859
 
9.3%
C 5193
 
6.1%
5 5099
 
6.0%
4 4468
 
5.3%
O 4408
 
5.2%
3 3972
 
4.7%
T 3879
 
4.6%
6 3313
 
3.9%
U 2860
 
3.4%
Other values (17) 28420
33.5%

E-Commerce Site
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size485.6 KiB
Snapdeal
2887 
Flipkart
2531 
Amazon
2301 

Length

Max length8
Median length8
Mean length7.4038088
Min length6

Characters and Unicode

Total characters57150
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSnapdeal
2nd rowSnapdeal
3rd rowSnapdeal
4th rowFlipkart
5th rowAmazon

Common Values

ValueCountFrequency (%)
Snapdeal 2887
37.4%
Flipkart 2531
32.8%
Amazon 2301
29.8%

Length

2025-01-23T17:35:09.432281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-23T17:35:09.567663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
snapdeal 2887
37.4%
flipkart 2531
32.8%
amazon 2301
29.8%

Most occurring characters

ValueCountFrequency (%)
a 10606
18.6%
p 5418
 
9.5%
l 5418
 
9.5%
n 5188
 
9.1%
S 2887
 
5.1%
d 2887
 
5.1%
e 2887
 
5.1%
F 2531
 
4.4%
i 2531
 
4.4%
k 2531
 
4.4%
Other values (6) 14266
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 10606
18.6%
p 5418
 
9.5%
l 5418
 
9.5%
n 5188
 
9.1%
S 2887
 
5.1%
d 2887
 
5.1%
e 2887
 
5.1%
F 2531
 
4.4%
i 2531
 
4.4%
k 2531
 
4.4%
Other values (6) 14266
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 10606
18.6%
p 5418
 
9.5%
l 5418
 
9.5%
n 5188
 
9.1%
S 2887
 
5.1%
d 2887
 
5.1%
e 2887
 
5.1%
F 2531
 
4.4%
i 2531
 
4.4%
k 2531
 
4.4%
Other values (6) 14266
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 10606
18.6%
p 5418
 
9.5%
l 5418
 
9.5%
n 5188
 
9.1%
S 2887
 
5.1%
d 2887
 
5.1%
e 2887
 
5.1%
F 2531
 
4.4%
i 2531
 
4.4%
k 2531
 
4.4%
Other values (6) 14266
25.0%

Category
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size515.0 KiB
Technology
2780 
Office Supplies
2505 
Furniture
2434 

Length

Max length15
Median length10
Mean length11.307294
Min length9

Characters and Unicode

Total characters87281
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTechnology
2nd rowFurniture
3rd rowFurniture
4th rowOffice Supplies
5th rowOffice Supplies

Common Values

ValueCountFrequency (%)
Technology 2780
36.0%
Office Supplies 2505
32.5%
Furniture 2434
31.5%

Length

2025-01-23T17:35:09.703222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-23T17:35:09.817572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
technology 2780
27.2%
office 2505
24.5%
supplies 2505
24.5%
furniture 2434
23.8%

Most occurring characters

ValueCountFrequency (%)
e 10224
 
11.7%
i 7444
 
8.5%
u 7373
 
8.4%
o 5560
 
6.4%
c 5285
 
6.1%
l 5285
 
6.1%
n 5214
 
6.0%
f 5010
 
5.7%
p 5010
 
5.7%
r 4868
 
5.6%
Other values (10) 26008
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87281
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 10224
 
11.7%
i 7444
 
8.5%
u 7373
 
8.4%
o 5560
 
6.4%
c 5285
 
6.1%
l 5285
 
6.1%
n 5214
 
6.0%
f 5010
 
5.7%
p 5010
 
5.7%
r 4868
 
5.6%
Other values (10) 26008
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87281
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 10224
 
11.7%
i 7444
 
8.5%
u 7373
 
8.4%
o 5560
 
6.4%
c 5285
 
6.1%
l 5285
 
6.1%
n 5214
 
6.0%
f 5010
 
5.7%
p 5010
 
5.7%
r 4868
 
5.6%
Other values (10) 26008
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87281
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 10224
 
11.7%
i 7444
 
8.5%
u 7373
 
8.4%
o 5560
 
6.4%
c 5285
 
6.1%
l 5285
 
6.1%
n 5214
 
6.0%
f 5010
 
5.7%
p 5010
 
5.7%
r 4868
 
5.6%
Other values (10) 26008
29.8%

Sub-Category
Categorical

High correlation 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size486.2 KiB
Copiers
953 
Bookcases
950 
Chairs
927 
Storage
876 
Phones
862 
Other values (12)
3151 

Length

Max length11
Median length10
Mean length7.486462
Min length3

Characters and Unicode

Total characters57788
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhones
2nd rowBookcases
3rd rowChairs
4th rowArt
5th rowStorage

Common Values

ValueCountFrequency (%)
Copiers 953
12.3%
Bookcases 950
12.3%
Chairs 927
12.0%
Storage 876
11.3%
Phones 862
11.2%
Accessories 533
6.9%
Appliances 475
6.2%
Machines 432
5.6%
Furnishings 334
 
4.3%
Art 302
 
3.9%
Other values (7) 1075
13.9%

Length

2025-01-23T17:35:09.964635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
copiers 953
12.3%
bookcases 950
12.3%
chairs 927
12.0%
storage 876
11.3%
phones 862
11.2%
accessories 533
6.9%
appliances 475
6.2%
machines 432
5.6%
furnishings 334
 
4.3%
art 302
 
3.9%
Other values (7) 1075
13.9%

Most occurring characters

ValueCountFrequency (%)
s 8677
15.0%
e 6846
11.8%
o 5200
 
9.0%
r 4543
 
7.9%
a 4325
 
7.5%
i 4322
 
7.5%
c 2923
 
5.1%
n 2836
 
4.9%
h 2555
 
4.4%
p 2458
 
4.3%
Other values (18) 13103
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57788
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 8677
15.0%
e 6846
11.8%
o 5200
 
9.0%
r 4543
 
7.9%
a 4325
 
7.5%
i 4322
 
7.5%
c 2923
 
5.1%
n 2836
 
4.9%
h 2555
 
4.4%
p 2458
 
4.3%
Other values (18) 13103
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57788
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 8677
15.0%
e 6846
11.8%
o 5200
 
9.0%
r 4543
 
7.9%
a 4325
 
7.5%
i 4322
 
7.5%
c 2923
 
5.1%
n 2836
 
4.9%
h 2555
 
4.4%
p 2458
 
4.3%
Other values (18) 13103
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57788
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 8677
15.0%
e 6846
11.8%
o 5200
 
9.0%
r 4543
 
7.9%
a 4325
 
7.5%
i 4322
 
7.5%
c 2923
 
5.1%
n 2836
 
4.9%
h 2555
 
4.4%
p 2458
 
4.3%
Other values (18) 13103
22.7%
Distinct2245
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Memory size661.9 KiB
2025-01-23T17:35:10.265327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length109
Median length88
Mean length30.371551
Min length5

Characters and Unicode

Total characters234438
Distinct characters82
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique934 ?
Unique (%)12.1%

Sample

1st rowSamsung Convoy 3
2nd rowSauder Facets Collection Library, Sky Alder Finish
3rd rowGlobal Push Button Manager's Chair, Indigo
4th rowNewell 330
5th rowAkro Stacking Bins
ValueCountFrequency (%)
fax 515
 
1.5%
with 489
 
1.4%
machine 442
 
1.3%
black 437
 
1.3%
red 387
 
1.1%
phone 383
 
1.1%
chair 369
 
1.1%
safco 353
 
1.0%
lockers 343
 
1.0%
set 336
 
1.0%
Other values (1969) 30324
88.2%
2025-01-23T17:35:10.730109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26602
 
11.3%
e 22053
 
9.4%
a 15132
 
6.5%
o 14109
 
6.0%
r 14054
 
6.0%
i 13408
 
5.7%
l 11028
 
4.7%
t 9853
 
4.2%
n 9115
 
3.9%
s 7594
 
3.2%
Other values (72) 91490
39.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 234438
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
26602
 
11.3%
e 22053
 
9.4%
a 15132
 
6.5%
o 14109
 
6.0%
r 14054
 
6.0%
i 13408
 
5.7%
l 11028
 
4.7%
t 9853
 
4.2%
n 9115
 
3.9%
s 7594
 
3.2%
Other values (72) 91490
39.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 234438
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
26602
 
11.3%
e 22053
 
9.4%
a 15132
 
6.5%
o 14109
 
6.0%
r 14054
 
6.0%
i 13408
 
5.7%
l 11028
 
4.7%
t 9853
 
4.2%
n 9115
 
3.9%
s 7594
 
3.2%
Other values (72) 91490
39.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 234438
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
26602
 
11.3%
e 22053
 
9.4%
a 15132
 
6.5%
o 14109
 
6.0%
r 14054
 
6.0%
i 13408
 
5.7%
l 11028
 
4.7%
t 9853
 
4.2%
n 9115
 
3.9%
s 7594
 
3.2%
Other values (72) 91490
39.0%

Sales
Real number (ℝ)

High correlation 

Distinct6169
Distinct (%)79.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean505.54721
Minimum1.728
Maximum1867.136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.4 KiB
2025-01-23T17:35:10.899645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.728
5-th percentile17.336
Q1236.961
median438.84
Q3713.20728
95-th percentile1236.465
Maximum1867.136
Range1865.408
Interquartile range (IQR)476.24628

Descriptive statistics

Standard deviation372.1854
Coefficient of variation (CV)0.73620305
Kurtosis0.793356
Mean505.54721
Median Absolute Deviation (MAD)231.72
Skewness0.95055385
Sum3902318.9
Variance138521.97
MonotonicityNot monotonic
2025-01-23T17:35:11.066375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.552 12
 
0.2%
17.94 7
 
0.1%
394.98 7
 
0.1%
14.94 6
 
0.1%
272.76 6
 
0.1%
366.84 6
 
0.1%
10.368 6
 
0.1%
12.96 6
 
0.1%
19.44 5
 
0.1%
333.96 5
 
0.1%
Other values (6159) 7653
99.1%
ValueCountFrequency (%)
1.728 1
< 0.1%
1.744 1
< 0.1%
2.22 1
< 0.1%
2.304 1
< 0.1%
2.6 1
< 0.1%
2.632 1
< 0.1%
2.688 1
< 0.1%
2.89 1
< 0.1%
2.944 1
< 0.1%
2.97 1
< 0.1%
ValueCountFrequency (%)
1867.136 1
< 0.1%
1860 1
< 0.1%
1859.55 1
< 0.1%
1859.13 1
< 0.1%
1855.68 1
< 0.1%
1854.495 1
< 0.1%
1849.32 1
< 0.1%
1847.34 1
< 0.1%
1845.72 1
< 0.1%
1843.17 1
< 0.1%

Quantity
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0296671
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.4 KiB
2025-01-23T17:35:11.217403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1368146
Coefficient of variation (CV)0.53027075
Kurtosis-0.2514218
Mean4.0296671
Median Absolute Deviation (MAD)2
Skewness0.70457992
Sum31105
Variance4.5659765
MonotonicityNot monotonic
2025-01-23T17:35:11.330818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 1631
21.1%
3 1582
20.5%
4 1172
15.2%
5 956
12.4%
6 629
 
8.1%
1 566
 
7.3%
7 544
 
7.0%
8 337
 
4.4%
9 235
 
3.0%
10 67
 
0.9%
ValueCountFrequency (%)
1 566
 
7.3%
2 1631
21.1%
3 1582
20.5%
4 1172
15.2%
5 956
12.4%
6 629
 
8.1%
7 544
 
7.0%
8 337
 
4.4%
9 235
 
3.0%
10 67
 
0.9%
ValueCountFrequency (%)
10 67
 
0.9%
9 235
 
3.0%
8 337
 
4.4%
7 544
 
7.0%
6 629
 
8.1%
5 956
12.4%
4 1172
15.2%
3 1582
20.5%
2 1631
21.1%
1 566
 
7.3%

Discount
Real number (ℝ)

Zeros 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.072549035
Minimum0
Maximum0.402
Zeros4504
Zeros (%)58.3%
Negative0
Negative (%)0.0%
Memory size60.4 KiB
2025-01-23T17:35:11.463732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.1
95-th percentile0.35
Maximum0.402
Range0.402
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.10902344
Coefficient of variation (CV)1.5027552
Kurtosis1.4627052
Mean0.072549035
Median Absolute Deviation (MAD)0
Skewness1.4896662
Sum560.006
Variance0.01188611
MonotonicityNot monotonic
2025-01-23T17:35:11.599629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 4504
58.3%
0.1 1033
 
13.4%
0.2 943
 
12.2%
0.4 287
 
3.7%
0.002 232
 
3.0%
0.15 188
 
2.4%
0.17 130
 
1.7%
0.07 79
 
1.0%
0.3 71
 
0.9%
0.27 69
 
0.9%
Other values (6) 183
 
2.4%
ValueCountFrequency (%)
0 4504
58.3%
0.002 232
 
3.0%
0.07 79
 
1.0%
0.1 1033
 
13.4%
0.15 188
 
2.4%
0.17 130
 
1.7%
0.2 943
 
12.2%
0.202 15
 
0.2%
0.25 65
 
0.8%
0.27 69
 
0.9%
ValueCountFrequency (%)
0.402 26
 
0.3%
0.4 287
 
3.7%
0.37 23
 
0.3%
0.35 52
 
0.7%
0.32 2
 
< 0.1%
0.3 71
 
0.9%
0.27 69
 
0.9%
0.25 65
 
0.8%
0.202 15
 
0.2%
0.2 943
12.2%

Profit
Real number (ℝ)

Zeros 

Distinct6138
Distinct (%)79.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.874722
Minimum-254.34
Maximum437.28
Zeros101
Zeros (%)1.3%
Negative1022
Negative (%)13.2%
Memory size60.4 KiB
2025-01-23T17:35:11.747900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-254.34
5-th percentile-69.52098
Q19.177
median58.23576
Q3144.42
95-th percentile312.03958
Maximum437.28
Range691.62
Interquartile range (IQR)135.243

Descriptive statistics

Standard deviation114.62825
Coefficient of variation (CV)1.3666602
Kurtosis0.70938687
Mean83.874722
Median Absolute Deviation (MAD)54.95976
Skewness0.61393802
Sum647428.98
Variance13139.635
MonotonicityNot monotonic
2025-01-23T17:35:11.895404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 101
 
1.3%
5.4432 11
 
0.1%
178.11 7
 
0.1%
17.58 6
 
0.1%
5.04 6
 
0.1%
45.36 6
 
0.1%
39.54 6
 
0.1%
93.6 6
 
0.1%
3.6288 6
 
0.1%
43.2 6
 
0.1%
Other values (6128) 7558
97.9%
ValueCountFrequency (%)
-254.34 1
< 0.1%
-252.9585 1
< 0.1%
-251.74512 1
< 0.1%
-250.82 1
< 0.1%
-248.052 2
< 0.1%
-244.944 1
< 0.1%
-241.296 1
< 0.1%
-241.2 1
< 0.1%
-238.5525 1
< 0.1%
-237.576 2
< 0.1%
ValueCountFrequency (%)
437.28 1
 
< 0.1%
436.94 1
 
< 0.1%
436.77 3
< 0.1%
435.888 1
 
< 0.1%
435.51 1
 
< 0.1%
434.4 2
< 0.1%
433.92 1
 
< 0.1%
433.44 1
 
< 0.1%
433.41 1
 
< 0.1%
433.26 1
 
< 0.1%

Shipping Cost
Real number (ℝ)

High correlation 

Distinct5807
Distinct (%)75.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.977191
Minimum1.04
Maximum195.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.4 KiB
2025-01-23T17:35:12.064468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.04
5-th percentile1.8
Q136.05
median51.008
Q378.1395
95-th percentile142.216
Maximum195.59
Range194.55
Interquartile range (IQR)42.0895

Descriptive statistics

Standard deviation40.540622
Coefficient of variation (CV)0.68739493
Kurtosis0.87469424
Mean58.977191
Median Absolute Deviation (MAD)17.835
Skewness0.93630894
Sum455244.94
Variance1643.542
MonotonicityNot monotonic
2025-01-23T17:35:12.230891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.33 11
 
0.1%
1.26 10
 
0.1%
1.27 10
 
0.1%
1.2 9
 
0.1%
1.71 9
 
0.1%
1.49 9
 
0.1%
1.31 9
 
0.1%
1.23 9
 
0.1%
1.41 9
 
0.1%
1.18 8
 
0.1%
Other values (5797) 7626
98.8%
ValueCountFrequency (%)
1.04 1
 
< 0.1%
1.06 2
 
< 0.1%
1.07 6
0.1%
1.08 1
 
< 0.1%
1.09 4
0.1%
1.1 3
< 0.1%
1.11 5
0.1%
1.12 3
< 0.1%
1.13 4
0.1%
1.14 5
0.1%
ValueCountFrequency (%)
195.59 1
< 0.1%
195.533 1
< 0.1%
195.371 1
< 0.1%
195.25 1
< 0.1%
195.24 1
< 0.1%
195.23 1
< 0.1%
194.97 1
< 0.1%
194.67 1
< 0.1%
194.6 1
< 0.1%
194.42 1
< 0.1%

Order Priority
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size469.9 KiB
Medium
3423 
High
2894 
Critical
939 
Low
463 

Length

Max length8
Median length6
Mean length5.3135121
Min length3

Characters and Unicode

Total characters41015
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowHigh
3rd rowHigh
4th rowHigh
5th rowLow

Common Values

ValueCountFrequency (%)
Medium 3423
44.3%
High 2894
37.5%
Critical 939
 
12.2%
Low 463
 
6.0%

Length

2025-01-23T17:35:12.380702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-23T17:35:12.629422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
medium 3423
44.3%
high 2894
37.5%
critical 939
 
12.2%
low 463
 
6.0%

Most occurring characters

ValueCountFrequency (%)
i 8195
20.0%
M 3423
8.3%
d 3423
8.3%
u 3423
8.3%
m 3423
8.3%
e 3423
8.3%
H 2894
 
7.1%
g 2894
 
7.1%
h 2894
 
7.1%
c 939
 
2.3%
Other values (8) 6084
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41015
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 8195
20.0%
M 3423
8.3%
d 3423
8.3%
u 3423
8.3%
m 3423
8.3%
e 3423
8.3%
H 2894
 
7.1%
g 2894
 
7.1%
h 2894
 
7.1%
c 939
 
2.3%
Other values (8) 6084
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41015
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 8195
20.0%
M 3423
8.3%
d 3423
8.3%
u 3423
8.3%
m 3423
8.3%
e 3423
8.3%
H 2894
 
7.1%
g 2894
 
7.1%
h 2894
 
7.1%
c 939
 
2.3%
Other values (8) 6084
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41015
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 8195
20.0%
M 3423
8.3%
d 3423
8.3%
u 3423
8.3%
m 3423
8.3%
e 3423
8.3%
H 2894
 
7.1%
g 2894
 
7.1%
h 2894
 
7.1%
c 939
 
2.3%
Other values (8) 6084
14.8%

Interactions

2025-01-23T17:34:59.948714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:56.268356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:56.996916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:57.714173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:58.501940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:59.244744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:35:00.071821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:56.398348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:57.103596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:57.918451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:58.618158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:59.357860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:35:00.195894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:56.517920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:57.231309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:58.030051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:58.737744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:59.475649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:35:00.299811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:56.638116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:57.343397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:58.133487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:58.862640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:59.582456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:35:00.442670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:56.763077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:57.473726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:58.270794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:58.983337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:59.699866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:35:00.563394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:56.869701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:57.588127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:58.381303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:59.099928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-01-23T17:34:59.825933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2025-01-23T17:35:12.730311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CategoryDiscountE-Commerce SiteMarketOrder PriorityProfitQuantityRegionRow IDSalesSegmentShip ModeShipping CostSub-Category
Category1.0000.2360.0000.2430.0430.1810.0550.2440.0850.2800.0220.0310.2550.999
Discount0.2361.0000.0000.3360.044-0.3970.0220.395-0.020-0.0250.0120.035-0.0440.214
E-Commerce Site0.0000.0001.0000.0200.0000.0240.0000.0000.0240.0000.0040.0000.0240.000
Market0.2430.3360.0201.0000.0810.2490.1610.9990.1530.3500.0110.0660.4330.293
Order Priority0.0430.0440.0000.0811.0000.1020.0400.0890.0840.1920.0200.2960.1220.076
Profit0.181-0.3970.0240.2490.1021.0000.1290.181-0.1120.4890.0000.0770.3870.141
Quantity0.0550.0220.0000.1610.0400.1291.0000.144-0.0680.2480.0000.0380.1820.082
Region0.2440.3950.0000.9990.0890.1810.1441.0000.1080.2350.0250.0780.2890.153
Row ID0.085-0.0200.0240.1530.084-0.112-0.0680.1081.000-0.2840.0300.073-0.5680.073
Sales0.280-0.0250.0000.3500.1920.4890.2480.235-0.2841.0000.0000.1590.7270.223
Segment0.0220.0120.0040.0110.0200.0000.0000.0250.0300.0001.0000.0110.0200.019
Ship Mode0.0310.0350.0000.0660.2960.0770.0380.0780.0730.1590.0111.0000.0980.065
Shipping Cost0.255-0.0440.0240.4330.1220.3870.1820.289-0.5680.7270.0200.0981.0000.204
Sub-Category0.9990.2140.0000.2930.0760.1410.0820.1530.0730.2230.0190.0650.2041.000

Missing values

2025-01-23T17:35:00.766522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-23T17:35:01.146585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Row IDOrder IDOrder DateShip DateShip ModeCustomer IDCustomer NameSegmentCityStateCountryRegionMarketProduct IDE-Commerce SiteCategorySub-CategoryProduct NameSalesQuantityDiscountProfitShipping CostOrder Priority
01CA-2014-AB10015140-419542014-11-112014-11-13First ClassAB-100151402Aaron BergmanConsumerOklahoma CityOklahomaUnited StatesCentral USUSCATEC-PH-5816SnapdealTechnologyPhonesSamsung Convoy 3221.98020.062.154440.77High
19CA-2014-AB10015140-419542014-11-112014-11-13First ClassAB-100151402Aaron BergmanConsumerOklahoma CityOklahomaUnited StatesCentral USUSCAFUR-BO-5957SnapdealFurnitureBookcasesSauder Facets Collection Library, Sky Alder Finish341.96020.054.713625.27High
210CA-2012-AB10015140-409742012-03-062012-03-07First ClassAB-100151404Aaron BergmanConsumerSeattleWashingtonUnited StatesWestern USUSCAFUR-CH-4421SnapdealFurnitureChairsGlobal Push Button Manager's Chair, Indigo48.71210.25.480111.13High
311CA-2012-AB10015140-409742012-03-062012-03-07First ClassAB-100151404Aaron BergmanConsumerSeattleWashingtonUnited StatesWestern USUSCAOFF-AR-5309FlipkartOffice SuppliesArtNewell 33017.94030.04.66444.29High
417CA-2012-AB10015140-409582012-02-192012-02-25Standard ClassAB-100151402Aaron BergmanConsumerArlingtonTexasUnited StatesCentral USUSCAOFF-ST-3078AmazonOffice SuppliesStorageAkro Stacking Bins12.62420.2-2.52481.97Low
522CA-2012-AB10015140-409742012-03-062012-03-07First ClassAB-100151404Aaron BergmanConsumerSeattleWashingtonUnited StatesWestern USUSCAOFF-ST-3744AmazonOffice SuppliesStorageCarina 42"Hx23 3/4"W Media Storage Unit242.94030.04.85881.28High
629CA-2012-AH10030140-410202012-04-212012-04-23Second ClassAH-100301406Aaron HawkinsCorporateTroyNew YorkUnited StatesEastern USUSCAOFF-FA-6129FlipkartOffice SuppliesFastenersStaples247.84080.0121.441655.20Critical
733CA-2013-AH10030140-416352013-12-272013-12-31Standard ClassAH-100301404Aaron HawkinsCorporateSan FranciscoCaliforniaUnited StatesWestern USUSCATEC-PH-4389AmazonTechnologyPhonesGeemarc AmpliPOWER60668.16090.275.168045.74Medium
835CA-2013-AH10030140-416352013-12-272013-12-31Standard ClassAH-100301404Aaron HawkinsCorporateSan FranciscoCaliforniaUnited StatesWestern USUSCAOFF-ST-4291FlipkartOffice SuppliesStorageFellowes Super Stor/Drawer Files323.10020.061.389026.70Medium
937CA-2012-AH10030140-410412012-05-122012-05-18Standard ClassAH-100301404Aaron HawkinsCorporateLos AngelesCaliforniaUnited StatesWestern USUSCAFUR-CH-4840AmazonFurnitureChairsIceberg Nesting Folding Chair, 19w x 6d x 43h279.45660.220.959211.69Medium
Row IDOrder IDOrder DateShip DateShip ModeCustomer IDCustomer NameSegmentCityStateCountryRegionMarketProduct IDE-Commerce SiteCategorySub-CategoryProduct NameSalesQuantityDiscountProfitShipping CostOrder Priority
77099989IN-2015-JS158807-421802015-06-252015-06-30Standard ClassJS-158807John StevensonConsumerBrisbaneQueenslandAustraliaOceaniaAsia PacificOFF-ST-5702SnapdealOffice SuppliesStorageRogers Lockers, Single Width571.45530.1215.86532.950Medium
77109990IN-2012-VP217307-411662012-09-142012-09-17Second ClassVP-217307Victor PreisHome OfficeBrisbaneQueenslandAustraliaOceaniaAsia PacificTEC-AC-4155SnapdealTechnologyAccessoriesEnermax Keyboard, Bluetooth75.11410.1-5.85632.940Critical
77119991MX-2013-JF1519026-413052013-01-312013-02-05Standard ClassJF-1519026Jamie FrazerConsumerSantiagoSantiagoChileSouth AmericaLATAMTEC-PH-5339AmazonTechnologyPhonesNokia Headset, Cordless151.92030.071.40032.938High
77129992CA-2012-CK12205140-410752012-06-152012-06-20Standard ClassCK-122051406Chloris KastensmidtConsumerHempsteadNew YorkUnited StatesEastern USUSCAOFF-AR-3517FlipkartOffice SuppliesArtBoston 16750 Black Compact Battery Pencil Sharpener35.00040.010.5001.720Medium
77139994ES-2014-HA1490564-418632014-08-122014-08-16Standard ClassHA-1490564Helen AbelmanConsumerGenoaLiguriaItalySouthern EuropeEuropeFUR-BO-5768FlipkartFurnitureBookcasesSafco Corner Shelving, Metal297.84020.074.46032.920Medium
77149995MX-2012-MS1777036-411932012-10-112012-10-16Standard ClassMS-1777036Maxwell SchwartzConsumerSanto DomingoSanto DomingoDominican RepublicCaribbeanLATAMOFF-AP-3584AmazonOffice SuppliesAppliancesBreville Toaster, Silver206.56050.248.96032.902High
77159996IN-2015-AS1013558-422292015-08-132015-08-18Standard ClassAS-1013558Adrian ShamiHome OfficeBilaspurUttar PradeshIndiaSouthern AsiaAsia PacificTEC-AC-4165AmazonTechnologyAccessoriesEnermax Memory Card, Programmable579.45050.0225.90032.900Medium
77169997RS-2015-NH8610108-423642015-12-262015-12-28Second ClassNH-8610108Nicole HansenCorporateYaroslavl'Yaroslavl'RussiaEastern EuropeEuropeTEC-CO-4576AmazonTechnologyCopiersHewlett Fax Machine, Digital319.56010.0146.97032.900Medium
77179998IN-2012-CA1277558-412142012-11-012012-11-06Standard ClassCA-1277558Cynthia ArntzenConsumerSatnaMadhya PradeshIndiaSouthern AsiaAsia PacificTEC-CO-5994AmazonTechnologyCopiersSharp Fax and Copier, Digital504.72030.0232.11032.890Medium
77189999ES-2013-MC1813045-415972013-11-192013-11-24Standard ClassMC-1813045Mike CaudleCorporateArgenteuilIle-de-FranceFranceWestern EuropeEuropeOFF-AR-3555SnapdealOffice SuppliesArtBoston Sketch Pad, Water Color414.24080.012.24032.890Medium